Tag Archive : Machine Learning

To be honest, designing business marketing campaigns for existing customers is as difficult as nailing jelly to a tree. That’s given.
To accomplish this feat of tough analysis, access to a list of customers, email addresses, and purchase data is imperative, but the tricky part remains unattended- sending meaningful insights that will boost a customer’s lifetime value and trigger a repeated purchase.

This trick of accurately targeting customers has been cracked by big enterprises with the help of in-house Data Science teams using a particular approach. The name of their approach is- Market Basket Analysis (MBA).
It is one of the key approaches adopted by renowned retailers to unravel the link between items. One of the basic principles of this approach is to track the combination of items that occur together repeatedly in the transactions.
In simple words, it enables retailers to determine the relationship between the items that consumers purchase. Let’s dig deep for a better understanding.

At the core

The very base of MBA, popularly known as Affinity Analysis, is depended on Data Mining, which uses Association Rule Learning to determine the bond between customers and the attributes associated with them.

The more common and stronger a relationship is, the quicker you can put your customers into segments for future analysis. All that is needed for this initiative is customer and order data.

A scenario– In a grocery store, there are numerous products, out of which consumers can lay their hands on any particular group of things. Say Peanut butter and jelly, cream cheese and turkey, etc. Using Market Basket Analysis on the grocery store data, it will be a piece of cake to determine which products are brought together by customers. Adding a feather in its cap is- discovering new bonds between customers and newer product combinations.

It comes as no surprise that MBA is applied to different segments of the retail sector to pump up sales and open new streams of revenue sources by determining the requirements of the customer and making purchase offers to them.

Cross-Selling: A sales technique that enables the seller to suggest a related product to a customer after the first purchase is made. With SPIN’s MBA, retailers can comprehend consumer behavior and pitch the right product for cross-selling.

Product Placement: It is a technique to place complementary and substitute goods together for the customer to buy them together. Using SPIN’s MBA, retailers can determine the goods, which a customer is more likely to buy together.

Detection of fraud: MBA contains credit card usage details and it can be used to determine the purchasing behavior to detect fraud possibility. SPIN’s MBA will prevent your retailer business from such adversities too.

Customer Behavior: SPIN’s MBA helps to comprehend customer behavior under a host of different conditions, enabling the retailer to determine the connection between two products, which people purchase, and get the knowledge of the customer’s buying behavior.

SPIN’s MBA is the combo of AI and ML

Businesses want to evaluate the different angles of customer behavior inside a store. With the right data sets to determine customer behavior of retail stores, businesses can categorize data to define the :

Right product association

Trip types

Point of sale and marketing

After analysis of the consumer behavior inside a retailer store, AI and ML techniques powered by SPIN Strategyalgorithms are applied to reap the following benefits for the retail business:

Develop lucrative combo offers

Place associate products together in the store

Customize the layout of the eCommerce site catalog

Manage inventory based on the products with better demand

Categorize different shopping trips to generate the best shopping experience

Create customer profiling and apply segmentation using buying pattern

Determining the best product association

To wrap it up

Market Basket Analysis is used by some of the biggest companies in the world to make informed and strategic business decisions.

Here at SPIN Strategy, our professionals can help you perform such an analysis on your customer base to drive your market growth and design your product.

In the true sense, Farming is by far one of the oldest lines of work in the world.

But, with the passage of millennia, Humanity has come a long way and so did agriculture. From conventional methods to grow crops to the usage of AI in Agriculture, Humanity has indeed taken a big leap.But with land getting in short supply and population growing by leaps and bounds, using creative methods to produce crops and boost productivity in limited space has become the need of the hour.

Change has stepped in. And this can be testified by the fact that the worldwide agriculture industry which is roughly estimated to be around $5 trillion, is stepping in the shoes of other sectors, shifting to what is known as Precision Farming.

For instance, adopting AI technologies to reap healthy crops, monitor soil, control pests, accumulate data for farmers etc. and eventually perk up a number of agriculture-related errands in the food supply chain.

Digital Agriculture: Farmers are using AI to increase crop yields

Artificial intelligence holds the promise of driving an agricultural revolution at a time when the world must produce more food using fewer resources.

Artificial Intelligence has various applications in agriculture ranging from rural automatons, facial acknowledgment, computerized water system frameworks, and driver less tractors. These applications are done in relationship with an alternate sort of sensors, GPS frameworks, radars, and other cutting edge contraptions dependent on AI.

Innovative progressions and the modernization of GPS are making ranchers and the agriculture specialist co-ops anticipate that additional upgrades will increase the profitability.Increasing adoption of the mechanical technology and IoT gadgets in agriculture is additionally assessed to drive the AI in agriculture.

Agriculture is slowly becoming digital and AI in agriculture is emerging in three major categories, (i) agricultural robotics, (ii) soil and crop monitoring, and (iii) predictive analytics.

Agricultural Robots – Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.

Predictive Analytics – Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.

How Analytics and AI steps in actually

Intelligent farming practices that have eventually transformed into knowledge-based agriculture, increases production levels and product quality to significant numbers.

With trained professionals in this art, companies like SPIN Strategy extracts insights from numerous data sources that are integrated into an Advanced Big Data Framework with data analysis decision-making, and automated data recording.

Result- Customized data for better plant health.

At SPIN, with the combination of smart farming and AI, we assemble, analyze, and digitize massive amounts of data to aid farmers to optimize their production systems. And, that’s why we like to be termed as the Farmer’s Little Hand.

With the use of technology, we:

Determine the ripeness of the crop

Help farmers preserve water

Customize production

Let’s dig deep to understand how ML and AI make a difference in Smart Farming using IoT.

Role of Artificial Intelligence in Agriculture

The agricultural industry is just like any other industry beginning to show interest in implementing the best-in-class technologies to save on resources and create more efficient processes. Agriculture is responsible for the survival of human beings, and the industry has made steady technological improvements in the last few years.

How SPIN’s ML and AI programs make the difference in Agriculture:

Machine Learning in Farming:

Provides faster and precise results by evaluating the Leaf Vein Morphology that has more data about the leaf properties.

Artificial Intelligence in Farming:

Uses algorithms and previous field data to determine crop performance in different environments, and builds a Probability Model to forecast the genes beneficial for the plant.

Here is a detailed overview, how SPIN’s AI programs turn the tables for agriculture:

1 . Water Management:

An AI-based application that can be connected with more successful use of irrigation systems and forecasting of daily dew point temperature, which is the base to determine any weather phenomena and analyze evaporation and transpiration.

2. Yield Prediction:

Moving beyond the traditional prediction of historical data, SPIN incorporates computer vision technologies to supply data on the go and conducts a thorough multidimensional analysis of weather, crops, economic conditions, etc. to reap the maximum benefit of the yield for farmers and the population.

3.Crop Quality:

The precise detection and categorization of the crop quality can shoot up the product price and cut down waste. Compared to human counterparts, machines avoid meaningless data to determine the quality of the crops and any possible anomalies.

4. Disease Detection:

At SPIN, we evaluate field images with Conventional Neural Networks to classify pests and diseases, track agro-technical activities, and gather data. To be more efficient, this approach needs more pesticides that lead to huge environmental expenses. ML is used as a general agriculture management to determine diseases and cut those costs.

5. Monitoring Crop’s health:

Hyper spectral imaging, together with sensing techniques and 3D laser scanning are vital to establish crop metrics across the land. SPIN crop health monitoring agent has the potential to change farmland monitoring by farmers and can significantly cut down on the effort.

How SPIN Contributes to Smart Farming

Confirmation and extensive testing of emerging AI applications in the Agriculture sector is estimated to be quite vital, since agriculture is affected by environmental factors that cannot be tamed, unlike other sectors where the risk is easy to predict.

At SPIN, we ensure a steady adoption of AI in agriculture with the help of Image Sensor Technology that helps in:

Real-time monitoring, analysis, and control of pest & disease

Pollination, Phrenology, Fertilization, Irrigation

Pollination, Phrenology, Fertilization, and Agri-Technical activities

Monitor and forecast yield performance in real-time to optimize results

Using Support Vector Machines to predict yield and crop quality

Using Artificial Neural Networks for crop management and weed detection

Scenario

Issue-One of our clients, a Colorado-based organization, wanted a preventive measure for defective crops, and optimize the potential for healthy crop production.

Solution– The trained AI professionals at SPIN conducted a comprehensive Soil Analysis and developed a system that will use Machine Learning to deliver clients with an idea of the soil’s strength and weakness. This way defective crop production could be prevented to a significant degree.

To conclude with

Artificial Intelligence and Farming have the potential to pave the way for an agricultural revolution, especially when the world needs more food production with limited resources.

As per the UN Food and Agriculture Organization, the population will hit the roof by 2 billion by 2050. However, experts are of the notion that only 4% of the additional land will fall under cultivation category. In tune with this, the use of the latest technology to do smart farming still takes the front seat.

AI-Powered solution will enable farmers to do more with limited resources and produce the finest quality of crops that amazes even the producer.

While the technology has embarked it’s impact outcomes to both general use and industrial use, Artificial Intelligence’s is a much-trusted solution to fight against terrorism globally.

Innovation has always helped humankind take a step ahead in the future and transfer knowledge into applications.

Machine automation along with a replica of the human intelligence to understand, learn and react has revolutionized industrial operations and a lot more. We very well know the fact that data is the fuel that drives most of the daily operations over the internet and into our daily lives. With more data, machines now can learn and adapt to its users.

In a recent conference at Minsk, organized by the United Nations Office of Counter-Terrorism (UNOCT) and Belarus, Artificial Intelligence was considered to be a cutting-edge solution for dealing with global terrorism. Vladimir Voronezh, Under-Secretary-General for the UN Counter-Terrorism Office, declared that the international community is utilizing artificial intelligence and machine learning capabilities to track down criminal and terrorism data globally.

The recent trends in Artificial Intelligence find its way to different sectors but mostly are leveraged by the government and law enforcement bodies to deal with national threats. This is itself a great leap for the technology. Here is a list of four latest trends that are today widely accepted by business owners, the government, security agencies, etc.

People Analytics: Pouring on the fuel of data, artificial intelligence can do miraculous things. Through AI, business and organizations can harness the data of people enrolled with them for any targeted campaign.

Algorithmic Website Personalization: Ever noticed how YouTube personalizes its home page as based on what you like or prefer to watch? This is all AI and machine learning.

Automated Customer Service: Retrieving customer data can help businesses provide the most relevant marketing campaigns, product display and a lot more. AI will help in customizing the customer experience.

Automated Resource Management: AI learns how to organize and allocate their subjects.

Given the present scenario, it is imperative to boost the exchange of expert knowledge on technologies such as Synthetic Biology, 3D Printing, Robotics, the synthesis of the human face

Nanotechnology, etc. to identify risks before-hand and respond in a jiffy.

At SPIN Strategy, we have experts of Robotics Process Automation, AI, and Machine Learning who can not only guide you in these complicated routes of business but also aid you in meeting your quest.

Before we dig deep into the nuts and bolts of MLP (Multilayer Perceptron), it is imperative to get the hang of neural network, to understand how it helps businesses get a major face lift.

Although the concept of the neural network is not likely to raise too many eyebrows out of ignorance, still people typically associate scientists conducting neurological research, with the term. In reality, a neural network is nothing but a smart system, which is steered by Artificial Intelligence (AI).

The most frequently used neural network these days, from the perspective of business, is MLP (Multilayer Perceptron). This neural network captures the most complex data and presents it in the simplest form, with the help of historical data.

Point to note- More than an individual, it’s the companies that get benefited out of MLP.

Companies prefer using MLP for business

If we go by market reports, companies use MLP in ways more than one, based on their business model. For instance, LinkedIn uses it to detect spam, while other companies use it to locate better products for it clients.

This overstates the fact that MLP is typically good for business. Read on to get convinced how MLP can benefit your business in ways you have not yet imagined.

Top 4 benefits of using MLP in business

AI and its application is prevalent everywhere. A smart business leader can never overlook the impact of AI and its applications, if his/her prime objective is to cope up with the changing business landscape.

Here are the top 4 reasons why MLP is a must in your business to cross its boundaries of growth.

Identifying patterns in images

With machines now able to identify images, business has taken a big leap, and almost every company worldwide, require pattern recognition facility.

Recommendation option

Most online companies make money via the recommendation of products/services, like Amazon, Netflix, Walmart and many more.

The fact that the use of recommendation engines opens doors for targeted and loyal customers along with increased sales, makes it all the more important. Now imagine these recommendation models being modified by more intelligent machine learning with the use of neural networks- Yes! more loyal customers and more sales.

Avoiding customer churning

Churn rate (percentage of customers who cease to use a product/service), is something every growing organization is worried of. For some, shrinking churn rate showcases a positive impact on the revenue chart.

Provided that a business is equipped with rich customer data, machine learning models together with MLP can help point out the complex usage patterns and find out the churning customers.

Advanced sales predictions

No matter businesses do not like to forecast sales, the fact that customers can be fickle and their preferences change, cannot be neglected. In tune with this, to tap into the new trends and moods of the customers, companies make predictions, which is highly time-consuming and hardly ever accurate.

If MLP is used to analyze huge data and make specific predictions, this could change the sales numbers of companies, in a good way.

The promising future of business with MLP

In the quest to come up with an efficient replicate of the human brain, MLP is a great step forward.

Covering the most arduous business area, MLP is the best tool for pattern recognition. On account of its increasing popularity, in the last few years a number of companies have invested in an artificial neural network. This a significant step to put an end to numerous business problems.

It is a time in history when devices that rove around the globe is empowered with a plethora of multi-functional technologies that can capture gestures, show clear readings to proximity temperature, etc. the list just goes on.

Internet of things is to be held accountable for such versatility, in bits and pieces, but does that clearly throw light upon the importance of this technology? With the ‘Big Bang’ of data and connected devices (the key foundation of IoT), the business sector is all warmed up.

However, the point of discussion over here is that in the absence of understanding to interpret data, and comprehend which one should take a lower priority, IoT is nothing but an unstructured flow of ineffective data.

The absence of AI deployment in IoTenabled devices stands as a ball and chain towards the blistering development of technology that put a veil on the real prize of humankind the astonishing transformation prospects, which IoT offers.

In support of this argument, market survey reports state that more and more organizations have turned to AI to not only improve but also change their business operations.

AI boosts IoT enabled devices

If a company has picked up pace in recent years, it is quite obvious to say that the business organization has inculcated rightful amalgamation of AI and IoT. For instance, Uber uses AI to connect the right passenger with the driver, thanks to customer behavior recognition and autonomous driving approach of data science.

For a highly operated IoT device, in order to comprehend what’s really taking place around a device and to respond dynamically, AI is the key tool. For this, AI needs to be deployed in the right manner, in a bid to realize the IoT offered benefits.

Key Considerations for using AI in IoT

To incorporate AI and derive maximum value from the data IoT makes available, business needs to consider some key steps.

Consider how to incorporate AI training into the process

Ensure the AI systems are constantly refined and enhanced

To conclude with AI in IoT

AI training is as crucial as algorithmic coding for traditional systems, however, in the present scenario that is a worldwide challenge.

With the right training model, IoT models can balance a pragmatic approach toward devices that have human intervention and contribution at the forefront.

If your business has IoT enabled devices, it is quite likely that you will aim to upgrade it with AI, but only with the helping hand of a professional.

Let’s get down straight down to business- the potential of Artificial Intelligencehas toppled human imagination, and for most of the organizations, it has been the real game changer. That’s given!

In this competitive era, when surpassing your industry peers in the rat race is the need of the hour, Artificial Intelligence and Machine Learning can really seal the deal. Be it using Artificial Intelligence to figure out the buying trends, comprehend personalization, customize supply, comprehend customer behavior or conduct financial trading, embracing Artificial Intelligence has no other alternative.

So, to use Artificial Intelligence algorithms as the perfect Competitive Differentiator and up the game for your business, it goes without saying that one needs to harness this technology to line up the strategies, join forces and come up with potential opportunities.

Here is the Do’s a business needs to take on to leverage Artificial Intelligence and acquire that competitive edge, among its peers.

Executive sponsorship is vital for ARTIFICIAL INTELLIGENCE

The importance of the term ‘Executive Sponsorship’ has further garnered momentum with the introduction of Artificial Intelligence and Machine Learning. Why?

The explanation is simple- the more engaged the C-suite members are with this technology, the chances to implement and going down the line with Analytics and Artificial Intelligence application across the organization steps up.

As per market reports, enterprises that have successfully implemented Artificial Intelligence and Machine Learning at a large scale confirm the staggering contribution of C-suite executives, as compared to the organizations where Artificial Intelligence is not prevalent.

Align investments, assets, and business plans with ARTIFICIAL INTELLIGENCE strategy

If a business intends to be one step ahead of its adversaries then it needs to be on its toes. In tune with this, a business needs to set the tone of the organization’s investment, assets, and resources with the Artificial Intelligence application strategy.

To cut the long story short- to increase the pace of growth, it is imperative for a business to align enterprise priorities with Artificial Intelligence projects, and voila! Victory.

Implement Agile Methodology to boost the growth of ARTIFICIAL INTELLIGENCE and Analytics

It is a known fact that traditional IT development, often, takes longer than expected and sometimes yields unsatisfactory outcomes. To be precise, less business wins for your company.

Enters Agile methodology, which enables a business to access all data from the data warehouse and open doors for analytics insights, giving your business the competitive edge it needs.

ARTIFICIAL INTELLIGENCE to help your competitive future

Businesses these days are adapting and are open to ideas and technological advancements that can turn the tables for them. The promising future of Artificial Intelligence and Analytics makes it a preferred choice among business leaders to incorporate it in the company operations.

Bottom line- business organizations that will give Artificial Intelligence applications a strategic priority is most likely to acquire a competitive advantage in the marketplace.

If you are motivated to leverage Artificial Intelligence for your business, get in touch with SPIN and it team members today, who can help you with your journey.

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SPIN Strategy is laser-focused on client satisfaction and provides the solutions to maximize profits from different avenues of business, eventually retaining the majority of the customers. We employ our technical and domain knowledge to create solutions to cater to clients from a multitude of domains and sizes.